C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
self.results = batch()
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
super().fit(
File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]
warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_search.py:918: UserWarning: One or more of the test scores are non-finite: [ nan 0.84778357 0.85119513 0.85885122 0.85882949 0.85966971
0.85967695 0.85543966 0.85969144 0.86137911 0.86052441 0.85627988
0.85543242 0.86137911 nan 0.84864552 0.85374475 0.85713458
0.85545415 0.85543242 0.85967695 0.85543242 0.85883674 0.86221208
0.85457048 0.85882949 0.85797479 0.85712009 nan 0.85714182
0.85544691 0.86309576 0.85798928 0.86223381 0.85714182 0.85711285
0.86393597 0.85798204 0.86220484 0.86053165 0.85796755 0.85882225
nan 0.85119513 0.85629436 0.85969144 0.85798928 0.85457048
0.86137911 0.8605389 0.85967695 0.86137187 0.85797479 0.86054614
0.85713458 0.85882949 nan 0.84693611 0.85545415 0.85798204
0.85799652 0.86394321 0.86138635 0.85798204 0.85796031 0.86306678
0.86306678 0.85712734 0.8596842 0.85882225]
warnings.warn(